Segmentation of an image of a semiconductor specimen

ABSTRACT

There is provided a system and method of segmenting an image of a fabricated semiconductor specimen. The method includes: obtaining a first probability map corresponding to the image representative of at least a portion of the fabricated semiconductor specimen and indicative of predicted probabilities of pixels in the image to correspond to one or more first structural elements presented in the image, obtaining a first label map informative of one or more segments representative of second structural elements and labels associated with the segments, performing simulation on the first label map to obtain a second probability map indicative of simulated probabilities of pixels in the first label map to correspond to the one or more segments, and generating a second label map based on the first probability map and the second probability map, the second label map being usable for segmentation of the image with enhanced repeatability.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a specimen, and more specifically, to segmentation ofan image of a specimen.

BACKGROUND

Current demands for high density and performance associated with ultralarge scale integration of fabricated devices require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. Such demands require formation of device features with highprecision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including automated examinationof the devices while they are still in the form of semiconductor wafers.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of wafer, masks, and other structures,combinations and/or parts thereof used for manufacturing semiconductorintegrated circuits, magnetic heads, flat panel displays, and othersemiconductor-fabricated articles.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of metrology-related operations, as well asoperations related to detection and/or classification of defects in aspecimen during its fabrication. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),sampling, reviewing, measuring, classifying and/or other operationsprovided with regard to the specimen or parts thereof using the same ordifferent inspection tools. Likewise, examination can be provided priorto manufacture of the specimen to be examined, and can include, forexample, generating an examination recipe(s) and/or other setupoperations. It is noted that, unless specifically stated otherwise, theterm “examination” or its derivatives used in this specification are notlimited with respect to resolution or size of an inspection area. Avariety of non-destructive examination tools includes, by way ofnon-limiting example, scanning electron microscopes, atomic forcemicroscopes, optical inspection tools, etc.

By way of non-limiting example, run-time examination can employ a twophase procedure, e.g. inspection of a specimen followed by review ofsampled locations of potential defects. During the first phase, thesurface of a specimen is inspected at high-speed and relativelylow-resolution. In the first phase, a defect map is produced to showsuspected locations on the specimen having high probability of a defect.During the second phase, at least some of the suspected locations aremore thoroughly analyzed with relatively high resolution. In some casesboth phases can be implemented by the same inspection tool, and, in someother cases, these two phases are implemented by different inspectiontools.

Examination processes are used at various steps during semiconductorfabrication to detect and classify defects on specimens, as well asperform metrology related operations. Effectiveness of examination canbe increased by automatization of process(es) as, for example, AutomaticDefect Classification (ADC), Automatic Defect Review (ADR), imagesegmentation, etc.

SUMMARY

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a method of segmenting an image of afabricated semiconductor specimen, the method performed by a processorand memory circuitry (PMC), the method comprising: obtaining a firstprobability map corresponding to the image representative of at least aportion of the fabricated semiconductor specimen and indicative ofpredicted probabilities of pixels in the image to correspond to one ormore first structural elements presented in the image, wherein the firstprobability map is generated by processing the image using a deeplearning model; obtaining a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; performing simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; andgenerating a second label map based on the first probability map and thesecond probability map, wherein the second label map is informative ofone or more segments representative of the first structural elements andlabels associated therewith, and wherein equivalent first structuralelements are associated with the same label, the second label map beingusable for segmentation of the image with enhanced repeatability.

In addition to the above features, the method according to this aspectof the presently disclosed subject matter can comprise one or more offeatures (i) to (xii) listed below, in any desired combination orpermutation which is technically possible:

-   (i). The performing simulation can comprise: performing a distance    transform on the first label map to obtain a distance map based on a    relative distance between each given pixel in the first label map    and a closest edge therefrom; and transforming the distance map into    the second probability map informative of simulated probabilities of    the pixels in the first label map to correspond to the one or more    segments as relative to the closest edge.-   (ii). The relative distance can include, for each given pixel in the    first label map: i) a distance between the given pixel and a closest    edge therefrom, and ii) a relative position of the given pixel with    respect to the closest edge.-   (iii). The relative position can indicate whether the given pixel is    located within a given second structural element whose contour    comprises the closest edge, or outside of the given second    structural element.-   (iv). The first label map can be generated based on at least one of    the following: the design data, the image, and the first probability    map.-   (v). The generating can comprise: combining the first probability    map and the second probability map to obtain a combined probability    map; and using a resolver to process the combined probability map,    giving rise to the second label map.-   (vi). The combining can be performed using a predetermined weight    factor indicative of a desired tradeoff level between sensitivity    and repeatability of the segmentation of the image.-   (vii). The resolver can be selected from a group comprising: DCRF,    Graph-cut and Hidden Markov Model (HMM).-   (viii). The resolver can be a DCRF resolver, and the combined    probability map can be processed based on a unary term and a    pairwise term.-   (ix). The pairwise term can be constructed based on an appearance    kernel and a smoothness kernel.-   (x). The appearance kernel can be an edge preserving denoiser that    determines a label of a given pixel based on similar neighboring    pixels thereof, and the smoothness kernel determines a label of a    given pixel based on neighboring pixels thereof-   (xi). The image can be a high-resolution review image of the    specimen obtained from a review tool.-   (xii). The second label map can have enhanced repeatability as    compared to a label map generated from the first probability map.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a system of segmenting an image of afabricated semiconductor specimen, the system comprising a processor andmemory circuitry (PMC) configured to: obtain a first probability mapcorresponding to the image representative of at least a portion of thefabricated semiconductor specimen and indicative of predictedprobabilities of pixels in the image to correspond to one or more firststructural elements presented in the image, wherein the firstprobability map is generated by processing the image using a deeplearning model; obtain a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; perform simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; and generatea second label map based on the first probability map and the secondprobability map, wherein the second label map is informative of one ormore segments representative of the first structural elements and labelsassociated therewith, and wherein equivalent first structural elementsare associated with the same label, the second label map being usablefor segmentation of the image with enhanced repeatability.

This aspect of the disclosed subject matter can comprise one or more offeatures (i) to (xii) listed above with respect to the method, mutatismutandis, in any desired combination or permutation which is technicallypossible.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform a method of segmenting an image of a fabricatedsemiconductor specimen, the method comprising: obtaining a firstprobability map corresponding to the image representative of at least aportion of the fabricated semiconductor specimen and indicative ofpredicted probabilities of pixels in the image to correspond to one ormore first structural elements presented in the image, wherein the firstprobability map is generated by processing the image using a deeplearning model; obtaining a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; performing simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; andgenerating a second label map based on the first probability map and thesecond probability map, wherein the second label map is informative ofone or more segments representative of the first structural elements andlabels associated therewith, and wherein equivalent first structuralelements are associated with the same label, the second label map beingusable for segmentation of the image with enhanced repeatability.

This aspect of the disclosed subject matter can comprise one or more offeatures (i) to (xii) listed above with respect to the method, mutatismutandis, in any desired combination or permutation which is technicallypossible.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a functional block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 2 illustrates a generalized flowchart of segmenting an image of afabricated semiconductor specimen in accordance with certain embodimentsof the presently disclosed subject matter.

FIG. 3 exemplifies a schematic diagram of segmenting an image of aspecimen in accordance with certain embodiments of the presentlydisclosed subject matter.

FIG. 4 illustrates an example of a simulation process from the firstlabel map to the second probability map in accordance with certainembodiments of the presently disclosed subject matter.

FIG. 5 illustrates an example of a distance transform from a first labelmap to a corresponding distance map in accordance with certainembodiments of the presently disclosed subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “generating”, “performing”,“obtaining”, “simulating”, “transforming”, “combining”, “using”,“processing”, “determining” or the like, refer to the action(s) and/orprocess(es) of a computer that manipulate and/or transform data intoother data, said data represented as physical, such as electronic,quantities and/or said data representing the physical objects. The term“computer” should be expansively construed to cover any kind ofhardware-based electronic device with data processing capabilitiesincluding, by way of non-limiting example, the examination system, thesegmentation system and respective parts thereof disclosed in thepresent application.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a specimen.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g. throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format, orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter.

The examination system 100 illustrated in FIG. 1 can be used forexamination of a semiconductor specimen (e.g. of a wafer and/or partsthereof) as part of the specimen fabrication process. The illustratedexamination system 100 comprises a computer-based system 101 capable ofautomatically determining metrology-related and/or defect-relatedinformation using images obtained during specimen fabrication (referredto hereinafter as fabrication process (FP) images or images). The system101 can be generally referred to as an FPEI (Fabrication ProcessExamination Information) system. According to certain embodiments of thepresently disclosed subject matter, the system 101 can be configured toperform segmentation of an FP image representative of at least a portionof a specimen, as will be described below in further detail withreference to FIG. 2. System 101 is thus also referred to as segmentationsystem in the present disclosure. System 101 can be operativelyconnected to one or more examination tools 120. The examination tools120 are configured to capture FP images and/or to review the captured FPimage(s) and/or to enable or provide measurements related to thecaptured image(s). The system 101 can be further operatively connectedto a design data server 110 and a storage unit 122.

For example, FP images (also referred to herein as images) can beselected from images of a specimen (e.g. wafer or parts thereof)captured during the manufacturing process, derivatives of the capturedimages obtained by various pre-processing stages (e.g. images of a partof a wafer or a photomask captured by SEM or an optical inspectionsystem, SEM images roughly centered around the defect to be classifiedby ADC, SEM images of larger regions in which the defect is to belocalized by ADR, registered images of different examination modalitiescorresponding to the same mask location, segmented images, height mapimages, etc.). It is to be noted that in some cases the images caninclude image data (e.g. captured images, processed images, etc.) andassociated numeric data (e.g. metadata, hand-crafted attributes, etc.).It is further noted that image data can include data related to a layerof interest and/or to one or more other layers of the specimen.

The term “examination tool(s)” used herein should be expansivelyconstrued to cover any tools that can be used in examination-relatedprocesses including, by way of non-limiting example, imaging, scanning(in a single or in multiple scans), sampling, reviewing, measuring,classifying and/or other processes provided with regard to the specimenor parts thereof. The one or more examination tools 120 can include oneor more inspection tools and/or one or more review tools. In some cases,at least one of the examination tools 120 can be an inspection toolconfigured to scan a specimen (e.g., an entire wafer, an entire die, orportions thereof) to capture inspection images (typically, at relativelyhigh-speed and/or low-resolution) for detection of potential defects. Insome cases, at least one of the examination tools 120 can be a reviewtool, which is configured to capture review images of at least some ofthe defects detected by inspection tools for ascertaining whether apotential defect is indeed a defect. Such a review tool is usuallyconfigured to inspect fragments of a die, one at a time (typically, atrelatively low-speed and/or high-resolution). Inspection tool and reviewtool can be different tools located at the same or at differentlocations, or a single tool operated in two different modes. In somecases at least one examination tool can have metrology capabilities.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools 120 can be implemented as inspectionmachines of various types, such as optical imaging machines, electronbeam inspection machines, and so on. In some cases the same examinationtool can provide low-resolution image data and high-resolution imagedata.

System 101 includes a processor and memory circuitry (PMC) 102operatively connected to a hardware-based I/O interface 126. PMC 102 isconfigured to provide all processing necessary for operating the system101 as further detailed with reference to FIG. 2 and comprises aprocessor (not shown separately) and a memory (not shown separately).The processor of PMC 102 can be configured to execute several functionalmodules in accordance with computer-readable instructions implemented ona non-transitory computer-readable memory comprised in the PMC. Suchfunctional modules are referred to hereinafter as comprised in the PMC.

Functional modules comprised in PMC 102 include a simulation module 104and a label map generation module 106. In certain embodiments, the labelmap generation module 106 can comprise a resolver module 108. The PMC102 can be configured to obtain, via I/O interface 126, a firstprobability map corresponding to an image representative of at least aportion of the fabricated semiconductor specimen and indicative ofpredicted probabilities of pixels in the image to correspond to one ormore first structural elements presented in the image. The firstprobability map can be generated by processing the image using a deeplearning model (e.g., the segmentation network 112). The PMC 102 can befurther configured to obtain a first label map informative of one ormore segments representative of second structural elements and labelsassociated with the segments. The second structural elements arepresented in design data charactering the at least portion. Equivalentsecond structural elements are associated with the same label. Thesimulation module 104 can be configured to perform simulation on thefirst label map to obtain a second probability map indicative ofsimulated probabilities of pixels in the first label map to correspondto the one or more segments. The label map generation module 106 can beconfigured to generate a second label map based on the first probabilitymap and the second probability map. The second label map is informativeof one or more segments representative of the first structural elementsand labels associated therewith. Equivalent first structural elementsare associated with the same label. The second label map can be usablefor segmentation of the image with enhanced repeatability. Operation ofsystem 101, PMC 102 and the functional modules therein will be furtherdetailed with reference to FIG. 2.

According to certain embodiments, the system 101 can be operativelyconnected with a deep learning model, such as, e.g., the segmentationnetwork 112 as illustrated. The segmentation network 112 can be a deepneural network (DNN) which includes layers organized in accordance withrespective DNN architecture. By way of non-limiting example, the layersof DNN can be organized in accordance with Convolutional Neural Network(CNN) architecture, Recurrent Neural Network architecture, RecursiveNeural Networks architecture, Generative Adversarial Network (GAN)architecture, or otherwise. Optionally, at least some of the layers canbe organized in a plurality of DNN sub-networks. Each layer of DNN caninclude multiple basic computational elements (CE) typically referred toin the art as dimensions, neurons, or nodes.

Generally, computational elements of a given layer can be connected withCEs of a preceding layer and/or a subsequent layer. Each connectionbetween a CE of a preceding layer and a CE of a subsequent layer isassociated with a weighting value. A given CE can receive inputs fromCEs of a previous layer via the respective connections, each givenconnection being associated with a weighting value which can be appliedto the input of the given connection. The weighting values can determinethe relative strength of the connections and thus the relative influenceof the respective inputs on the output of the given CE. The given CE canbe configured to compute an activation value (e.g. the weighted sum ofthe inputs) and further derive an output by applying an activationfunction to the computed activation. It is noted that the teachings ofthe presently disclosed subject matter are not bound by specificarchitecture of the DNN.

In some embodiments, system 101 can further comprise the segmentationnetwork 112 or part thereof. In other words, the respective functions ofthe segmentation network 112 can, at least partly, be integrated withinsystem 101.

Additionally to the segmentation system 101, the examination system 100can comprise one or more examination modules, such as, e.g., defectdetection module and/or Automatic Defect Review Module (ADR) and/orAutomatic Defect Classification Module (ADC) and/or metrology-relatedmodule and/or other examination modules. Such examination modules canutilize the output of the segmentation system 101 for examination of asemiconductor specimen. In some cases, the one or more examinationmodules can be at least partially integrated with the one or moreexamination tools 120.

According to certain embodiments, the system 101 can be operativelyconnected with a design data server 110 (e.g., CAD server) via thehardware-based I/O interface 126. The design data server 110 isconfigured to store and provide design data characterizing the specimen.The design data of the specimen can be in any of the following formats:the physical design layout (e.g., CAD clip) of the specimen, a rasterimage, and a simulated image derived from the design layout. Accordingto certain embodiments, a first label map informative of one or moresegments representative of second structural elements (i.e., structuralelements presented in the design data) and labels associated therewithcan be derived, e.g., from the design data, and stored in the designdata server 110 or the storage unit 122, and the I/O interface 126 canbe configured to receive the first label map therefrom. Alternatively,the I/O interface 126 can receive, from the design data server 110,design data characterizing at least a given portion of the specimen, andprovide to the PMC 102 to process the design data to derive the firstlabel map.

According to certain embodiments, system 101 can comprise a storage unit122. The storage unit 122 can be configured to store any data necessaryfor operating system 101, e.g., data related to input and output ofsystem 101, as well as intermediate processing results generated bysystem 101. By way of example, the storage unit 122 can be configured tostore images and/or derivatives thereof produced by the examination tool120. Accordingly, the one or more images can be retrieved from thestorage unit 122 and provided to the PMC 102 for further processing. Thestorage unit 122 can also be configured to store design datacharacterizing the specimen and/or derivatives thereof.

In some embodiments, system 101 can optionally comprise a computer-basedGraphical User Interface (GUI) 124 which is configured to enableuser-specified inputs related to system 101. For instance, the user canbe presented with a visual representation of the specimen (for example,by a display forming part of GUI 124), including image data and/ordesign data of the specimen. The user may be provided, through the GUI,with options of defining certain operation parameters. The user may alsoview the operation results, such as, e.g., the segmentation output, onthe GUI.

As aforementioned, system 101 is configured to receive, via I/Ointerface 126, input data including a first probability mapcorresponding to an image representative of at least a portion of thespecimen and a first label map informative of one or more segmentsrepresentative of second structural elements presented in the designdata and labels associated therewith. System 101 is further configuredto process at least part of the received input data and send, via I/Ointerface 126, the results (or part thereof) to the storage unit 122,and/or the examination tool(s) 120, and/or GUI 124 (for rendering theresults) and/or external systems (e.g. Yield Management System (YMS) ofa FAB).

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and/or hardware.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in another embodimentsat least part of examination tools 120, storage unit 122 and/or GUI 124can be external to the examination system 100 and operate in datacommunication with system 101 via I/O interface 126. System 101 can beimplemented as stand-alone computer(s) to be used in conjunction withthe examination tools. Alternatively, the respective functions of thesystem 101 can, at least partly, be integrated with one or moreexamination tools 120, thereby facilitating and enhancing thefunctionalities of the examination tools 120 in examination relatedprocesses.

Referring to FIG. 2, there is illustrated a generalized flowchart ofsegmenting an image of a fabricated semiconductor specimen in accordancewith certain embodiments of the presently disclosed subject matter.

A first probability map can be obtained (202) (e.g., by the PMC 102 viathe I/O interface 126). The first probability map corresponds to theimage representative of at least a portion of the fabricatedsemiconductor specimen and indicates predicted probabilities of pixelsin the image to correspond to one or more first structural elementspresented in the image.

The image can be a FP image of a specimen obtained in a fabricationprocess thereof. By way of non-limiting example, the image can beobtained by examining a specimen using one or more inspection tools(e.g., low-resolution examination machines, such as, an opticalinspection system, low-resolution SEM, etc.). Alternatively oradditionally, the image can be obtained by examining the specimen usingone or more review tools (e.g., high-resolution examination machines toreview a subset of potential defect locations selected for review, suchas a scanning electron microscope (SEM), Atomic Force Microscopy (AFM),etc.).

The first probability map can be generated by processing the image usinga deep learning model (e.g., a DNN such as the segmentation network 112as illustrated). Generally speaking, segmentation may refer to anyprocess of partitioning an image into meaningful parts/segments (forexample, background and foreground, noisy and non-noisy areas,structural elements, defect and non-defect, etc.) whilst providingper-pixel or per-region values indicative of such segments. According tocertain embodiments of the presently disclosed subject matter, thesegmentation network 112 can be configured to segment the imageaccording to one or more structural elements presented in the image(also referred to herein as first structural elements, or image-basedstructural elements). The output of the segmentation network (i.e., thefirst probability map) can be a segmentation probability map in whichthe value of each pixel/element is indicative of a predicted probabilityof a corresponding pixel in the image to correspond/belong to thestructural elements in the image, or, say, to belong to one or moresegments representative of the structural elements in the image. In somecases, the image to be fed into the DNN can be informative of aplurality of layers of the specimen, and the first probability map canbe generated for each layer.

A structural element used herein can refer to any original object on theimage data or design data that has a geometrical shape or geometricalstructure with a contour, or a geometrical shape combined with otherobject(s). A structural element that is located/presented on the imagedata can be referred to as an image-based structural element (alsoreferred to herein as a first structural element). A structural elementthat is located/presented on the design data can be referred to as adesign-based structural element (also referred to herein as a secondstructural element). A structural element can be presented, e.g., in theform of a polygon. A structural element can be defined by the user, orcan be defined automatically, for example using rule-based ormachine-learning techniques.

The first probability map is usable for generating a segmentation labelmap comprising per-pixel or per-region segmentation labels (alsoreferred to herein as labels) indicative of different segments on theimage. In some embodiments, each segmented structural element (and/orpixels thereof) can be assigned with a segmentation label, andequivalent structural elements on the image can be associated with thesame label. Equivalent structural elements can refer to structuralelements that correspond to the same design pattern, irrespective of theorientation thereof. By way of example, the structural elements (e.g.,the polygons) on one layer can have one segmentation label, and thepolygons on another layer can have a different segmentation label, whilethe background can have a separate segmentation label.

For purpose of illustration, reference is also made to FIG. 3,exemplifying a schematic diagram of segmenting an image of a specimen inaccordance with certain embodiments of the presently disclosed subjectmatter. As shown, an image 302 of a specimen captured by an examinationtool 120 is received and fed into a segmentation network 112 forprocessing, giving rise to a first probability map 306, as describedabove. It is to be noted that in some cases, the image 302 can beinformative of multiple layers of the specimen, where each layercontains different types of structural elements. In such cases, multiplefirst probability maps 306 can be generated corresponding to respectivelayers of the specimen.

According to certain embodiments of the presently disclosed subjectmatter, a segmentation probability map generated using a segmentationnetwork by processing an image of a specimen can result in relativelysensitive segmentation output. By way of example, the segmentationnetwork can be designed with a specific architecture and/or trained inspecific ways for the purpose of enhancing segmentation sensitivity. Insuch cases, the segmentation probability map, and/or the segmentationlabel map derived therefrom, can result in high sensitivity. By way ofexample, the segmentation output may tightly follow certain imagecharacteristics which may be affected by physical processes, such asimaging conditions, tool/process variations etc. For instance, thesegments as indicated in the segmentation label map can have highaccuracy and consistency with respect to the structural elementspresented in the image (e.g., the contours of the segments closely matchthe polygons in the image, thus the edges can be rough/blurry, andcertain pixels/areas within a polygon can be assigned with segmentationlabels that are inconsistent with the neighboring pixels, etc.). In suchcases, different images captured for the same location of the samespecimen may result in different segmentation outcomes due to thevariances presented in the images. Such segmentation outcomes are lessdesired since customers require repeatable segmentation (also termedprecise segmentation) for the same specimen, irrespective of variancescaused in different images. According to certain embodiments of thepresently disclosed subject matter, there is proposed a method andsystem of using design-based data (i.e., generating a simulatedprobability map using a design label map) to influence the imagesegmentation, thereby improving repeatability/precision of thesegmentation, as described in further detail below.

In addition to the first probability map, a first label map can also beobtained (204) (e.g., by the PMC 102 via the I/O interface 126). Thefirst label map can be a segmentation label map informative of one ormore segments representative of second structural elements and labelsassociated with the segments. The second structural elements, asmentioned above, are design-based structural elements presented indesign data charactering the at least portion of the specimen.Similarly, equivalent second structural elements are associated with thesame label. In other words, the first label map can be referred to as adesign label map since the segments thereof correspond to thedesign-based structural elements. An example of the first label map isillustrated in FIG. 3 as 304. As shown in the present example, the firstlabel map 304 presents two segments (differentiated by different graylevels): one segment corresponds to the structural elements (e.g., thecolumns as illustrated) comprised therein, and the other segmentcorresponds to the background.

It is noted that although the first label map is informative of segmentscorresponding to design-based structural elements, it does notnecessarily have to be generated based on design data. According tocertain embodiments, the first label map can be generated based on atleast one of the following: the design data, the image, and the firstprobability map. By way of example, the first label map can be generatedbased on design data, e.g., by performing simulation on the design datasuch that the design-based structural elements thereof are associatedwith respective segmentation labels. In some cases, such simulation alsotakes into consideration the difference between the design-basedstructural elements and the corresponding image-based structuralelements. For instance, in some cases, due to the conditions of thedesign tool and/or the imaging tool, a design-based structural elementsuch as a polygon may actually appear in the shape of a circle in theimage. The simulation can take it into consideration and generate asimulated label map including the structural element with a simulatedshape as it would have appeared in the image.

By way of another example, the first label map can be generated based onthe image. For instance, the label map can be created manually inaccordance with the appearance of the structural elements in a SEMimage. By way of a further example, the first label map can be generatedbased on the first probability map. For instance, the first label mapcan be generated by identifying repetitive structural elements/patternsin the first probability map, averaging the repetitive patterns toobtain a common pattern having a smoother shape without variation, andplacing the common pattern in the positions of the respective repetitivepatterns.

In some cases, the first label map can be pre-generated in accordancewith the various generation methods described above and stored in thestorage unit 122 or the design data server 110 to be provided to the PMC102 for further processing. Alternatively, in some other cases, thegeneration process can be performed by the PMC 102 (i.e., thefunctionality of the label map generation can be integrated into PMC102) as a pre-processing step.

Upon obtaining the first label map, simulation can be performed (206)(e.g., by the simulation module 104) on the first label map to obtain asecond probability map indicative of simulated probabilities of pixelsin the first label map to correspond to the one or more segments. Thesecond probability map is also referred to herein as a simulatedprobability map. As illustrated in FIG. 3, the probability simulation isperformed on the first label map 304, giving rise to a simulatedprobability map 308.

According to certain embodiments, the simulation from the first labelmap to the second probability map can include a distance transform andprobability transform. Specifically, a distance transform can beperformed on the first label map to obtain a distance map. The distancetransform can be based on a relative distance between each given pixelin the first label map and a closest edge therefrom. The distance mapcan then be transformed into the second probability map informative ofsimulated probabilities of the pixels in the first label map tocorrespond to the one or more segments as relative to the closest edge.

Turning now to FIG. 4, there is illustrated an example of a simulationprocess from the first label map to the second probability map inaccordance with certain embodiments of the presently disclosed subjectmatter.

As shown, a first label map 402 is generated in accordance with thegeneration methods as described above, to represent segmentscorresponding to design-based structural elements 404. While notnecessarily so, the first label map 402 can be separated into one ormore layers according to the design (e.g., two layers in the presentexample, the background layer 406 and the foreground layer 408, i.e.,the layer with the structural elements 404). A distance transform isperformed respectively on the two layers 406 and 408, giving rise to twocorresponding distance maps 410 and 412. For better illustration of thedistance transform, attention is now directed to FIG. 5.

Referring now to FIG. 5, there is illustrated an example of a distancetransform from a first label map to a corresponding distance map inaccordance with certain embodiments of the presently disclosed subjectmatter.

As shown, a first label map 502 (or one layer separated therefrom) canbe presented in the form of a binary map 504, in which the value of “1”represents a label of a segment corresponding to a structural element501, and the value of “0” represents a label of the background.Therefore, in the binary map, the pixels of “1” (such as, e.g., pixel510) next to the pixels of “0” can indicate an edge of the structuralelement. The distance transform can be performed on the binary map 504to obtain a distance map 506 based on a relative distance between eachgiven pixel in the binary map 504 and a closest edge therefrom. By wayof example, the relative distance includes, for each given pixel in thefirst label map: i) a distance between the given pixel and a closestedge therefrom, and ii) a relative position of the given pixel withrespect to the closet edge. The relative position can indicate whetherthe given pixel is located within a given second structural elementwhose contour comprises the closest edge (i.e., within the contour), oroutside of the given second structural element (i.e., outside thecontour). For instance, for a given pixel 508, the closest edge isindicated by the diagonal pixel 510 whose value is “1”. Thus thedistance between the pixels 508 and 510 can be calculated (e.g., 1.4 inthe present example). Considering the pixel 508 is outside of thestructural element 501, a positive value of 1.4 can be assigned in thecorresponding position 509 in the distance map 506. Otherwise, in thecase of a given pixel being located within a given structural element,such as, e.g., pixel 512, a negative value can be assigned in thedistance map to indicate this relative position. Similarly, suchdistance transform can be performed for all the pixels in the binary map504, and a corresponding distance map 506 (and correspondingrepresentative image 503) can be generated. It is to be noted that thespecific numbers, positive and negative values, as well as thecalculation methods of the distances, are for purpose of exemplificationand illustration only, and should not be construed to limit the presentdisclosure in any way.

As can be seen from the example of FIG. 5, the distance map generatedfrom the distance transform is informative of a relative distancebetween each pixel and a nearby edge thereof (e.g., in terms of thedistance therebetween and a relative location with respect to the nearbyedge). Referring back to FIG. 4, a probability transform is thenperformed respectively on the two distance maps 410 and 412 generated asdescribed with reference to FIG. 5, giving rise to two probability maps414 and 416. By way of example, the probability transform can includenormalizing the values in the distance maps to the range of [0, 1]. Forinstance, one way of normalization is to calculate a correspondingprobability value (denoted as p) as an exponential function of therelative distance (denoted as d), such as, e.g., p=e^(−d), or p=e^(−d) ²(also referred to herein as normalization function).

In a probability map generated in such a way, such as, e.g., theprobability map 416, the pixels located in the central region of astructural element typically have high probability values such as 1 orclose to 1. As moving from the center towards the edge of the structuralelement, the probability values of the pixels gradually decrease. Whenreaching a certain point outside of the edge, the probability valueeventually reduces to 0. The probability map 416 shows such a gradualchange of the probability values (illustrated as a ramp 418 of pixelvalues changing from 1 to 0). It is to be noted that the aboveexemplified normalization functions are listed for purpose ofillustration only and should not be regarded as limiting the presentdisclosure in any way. Any other suitable functions can be used in lieuof the above. In some cases, the exponential function used in thenormalization can possibly combine one or more additional parameterswhich may indicate certain characteristics of the distance map and canbe used for tuning the probability normalization. One example of such anadditional parameter can be a parameter indicative of, e.g., thesmoothness of the distance map, and can be used to tune the steepness ofthe transition from 1 to 0 in the probability map. Optionally, theprobability maps 414 and 416 corresponding to the two layers can besummed/combined into one probability map for further processing, e.g.,by applying an argmax function on the two probability maps.

Continuing with the description of block 206 of FIG. 2, the secondprobability map generated in accordance with the above description ofFIG. 5 can be indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments represented inthe first label map. The probability simulation takes into considerationpossible effects caused by a physical process, such as, e.g., processvariation. The simulated probabilities reflect the likelihood of thepixels in the first label map to belong to the “real life” segments(i.e., how these segments would actually appear after e.g., processvariation) which correspond to the segments represented in the firstlabel map. By way of example, the simulated probability map indicates anunderlying principle that the pixels in the center of a structuralelement would theoretically have higher probability of belonging to asegment corresponding to the structural element as compared to thepixels surrounding the contours of the structural element (e.g., pixelsthat are further away from the center and pixels that are locatedoutside of contour of the structural element).

A second label map can be generated (208) (e.g., by the label mapgeneration module 106) based on the first probability map and the secondprobability map. The second label map is informative of one or moresegments representative of the first structural elements (i.e.,image-based structural elements) and labels associated therewith.Equivalent first structural elements are associated with the same label.The second label map is an improved segmentation label map as comparedto a segmentation label map generated from only the first probabilitymap, and can be usable for segmentation of the image with enhancedrepeatability/precision.

According to certain embodiments, generation of the second label map cancomprise: combining the first probability map and the second probabilitymap to obtain a combined probability map, and using a solver to processthe combined probability map to give rise to a second label map. Asolver generally refers to an algorithm that takes a problem descriptionin a generic form and calculates a solution for the problem. In thepresent subject matter, the solver refers to an algorithm that, based onthe input information of the probability maps (in some cases also theimage), decides where to place the border of separation of the segments.In some cases, the combining of the first probability map and the secondprobability map can be performed using a predetermined weight factorindicative of a desired tradeoff level between sensitivity andrepeatability of the segmentation of the image. By way of example, insome cases, such a weight factor can be determined, e.g., by thecustomer, according to certain specification requirements. In somecases, such a weight factor can be selected from a group of candidatefactors. By applying a weighted combination using the weight factor, theamount of design-based data influence on the image-based data can becontrolled and a desired balance between sensitivity and repeatabilityof the segmentation can be achieved.

According to certain embodiments, the solver can be selected from agroup comprising: dense conditional random field (DCRF), Graph-cut andHidden Markov Model (HMM). In one embodiment, the solver can be a DCRFsolver. The DCRF solver can accept a single data term in the shape ofprobabilities (e.g., the combined probability map) and generate a labelmap determining which label to assign to each pixel. In some otherembodiments, in addition to the combined probability map, the DCRF canaccept the image as an additional input, and generate a label map basedon both inputs.

In some embodiments of the present disclosure, the DCRF can be used toprocess the combined probability map based on a unary term and apairwise term. The DCRF applies an iterative algorithm that converges toa local minimum after N iterations (N can be predefined). A localminimum means that a segmentation label map is generated in which thegiven labels minimize an error function E(x) related to the unary termand the pairwise term.

By way of example, the unary term can be based on the probability of thepixel to be related to a specific segment. For instance, the unary termcan be a function of argmax( ), which outputs the segment with thehighest probability for this pixel. The pairwise term can be constructedfrom two adversary terms. For instance, the pairwise term can beconstructed based on an appearance kernel and a smoothness kernel. Theappearance kernel can be an edge preserving denoiser (also termed as abilateral filter) that determines a label of a given pixel based onsimilar neighboring pixels thereof (i.e., it determines the label of thepixel by looking at its nearby neighbors that look alike (e.g., share asimilar probability)). The outcome is that the appearance kernelsmoothens the probability map but preserves sharp edges betweensegments. By way of example, the appearance kernel can be implemented asan exponential function related to the relative locations and thesimilarities between neighboring pixels. The smoothness kerneldetermines a label of a given pixel based on neighboring pixels thereof.It smoothens the probability map without preserving edges. By way ofexample, the smoothness kernel can be implemented as an exponentialfunction related to the relative locations between pixels.

By applying both kernels, the combined probability map can be smoothenedfrom at least the following two aspects: i) the probabilities within thecontour of each structural element can be smoothened, which is based onthe assumption that pixels within a polygon should normally belong tothe same segment; and ii) the probabilities along the contour of eachstructural element can be smoothened while preserving the edges, whichis based on the assumption that polygons normally have smooth but notrough contours.

It is to be noted that DCRF is described herein as one example of amodel usable for processing the combined probability map to obtain asmoothened segmentation label map, and should not be regarded aslimiting the present disclosure in any way. Other suitable models and/ortools and/or methods usable for image smoothing and image segmentation,such as, e.g., Graph-cut and Hidden Markov Model (HMM), can be used inaddition to, or in lieu of, the above.

Among advantages of certain embodiments of the segmentation process asdescribed herein is using design-based data derived in a specific way(e.g., simulated probability map derived from a design label map) toinfluence segmentation of an image of a fabricated semiconductorspecimen, thereby improving repeatability and precision of thesegmentation outcome. The influence can be achieved by combining thesimulated probability map with the probability map obtained fromprocessing the image, and deriving an enhanced segmentation label map byprocessing the combined probability map. The enhanced segmentation canbe used for different applications, such as, e.g., ADC, ADR, defectdetection, matching, metrology and other examination tasks.

As illustrated in FIG. 3, the two probability maps 306 and 308 arecombined using a weight factor θ (illustrated as 310), and the combinedprobability map is provided as input to DCRF 312 to be processed. As aresult, a second label map 314 with enhanced repeatability is derived.The second label map 314 has enhanced repeatability as compared to alabel map generated from only the first probability map 306.

It is to be understood that the present disclosure is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings.

It will also be understood that the system according to the presentdisclosure may be, at least partly, implemented on a suitably programmedcomputer. Likewise, the present disclosure contemplates a computerprogram being readable by a computer for executing the method of thepresent disclosure. The present disclosure further contemplates anon-transitory computer-readable memory tangibly embodying a program ofinstructions executable by the computer for executing the method of thepresent disclosure.

The present disclosure is capable of other embodiments and of beingpracticed and carried out in various ways. Hence, it is to be understoodthat the phraseology and terminology employed herein are for the purposeof description and should not be regarded as limiting. As such, thoseskilled in the art will appreciate that the conception upon which thisdisclosure is based may readily be utilized as a basis for designingother structures, methods, and systems for carrying out the severalpurposes of the presently disclosed subject matter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of thepresent disclosure as hereinbefore described without departing from itsscope, defined in and by the appended claims.

1. A computerized method of segmenting an image of a fabricatedsemiconductor specimen, the method performed by a processor and memorycircuitry (PMC), the method comprising: obtaining a first probabilitymap corresponding to the image representative of at least a portion ofthe fabricated semiconductor specimen and indicative of predictedprobabilities of pixels in the image to correspond to one or more firststructural elements presented in the image, wherein the firstprobability map is generated by processing the image using a deeplearning model; obtaining a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; performing simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; andgenerating a second label map based on the first probability map and thesecond probability map, wherein the second label map is informative ofone or more segments representative of the first structural elements andlabels associated therewith, and wherein equivalent first structuralelements are associated with the same label, the second label map beingusable for segmentation of the image with enhanced repeatability.
 2. Thecomputerized method according to claim 1, wherein the performingsimulation comprises: performing a distance transform on the first labelmap to obtain a distance map based on a relative distance between eachgiven pixel in the first label map and a closest edge therefrom; andtransforming the distance map into the second probability mapinformative of simulated probabilities of the pixels in the first labelmap to correspond to the one or more segments as relative to the closestedge.
 3. The computerized method according to claim 1, wherein therelative distance includes, for each given pixel in the first label map:i) a distance between the given pixel and a closest edge therefrom, andii) a relative position of the given pixel with respect to the closestedge.
 4. The computerized method according to claim 3, wherein therelative position indicates whether the given pixel is located within agiven second structural element whose contour comprises the closestedge, or outside of the given second structural element.
 5. Thecomputerized method according to claim 1, wherein the first label map isgenerated based on at least one of the following: the design data, theimage, and the first probability map.
 6. The computerized methodaccording to claim 1, wherein the generating comprises: combining thefirst probability map and the second probability map to obtain acombined probability map; and using a resolver to process the combinedprobability map, giving rise to the second label map.
 7. Thecomputerized method according to claim 6, wherein the combining isperformed using a predetermined weight factor indicative of a desiredtradeoff level between sensitivity and repeatability of the segmentationof the image.
 8. The computerized method according to claim 6, whereinthe resolver is selected from a group comprising: dense conditionalrandom field (DCRF), Graph-cut and Hidden Markov Model (HMM).
 9. Thecomputerized method according to claim 6, wherein the resolver is a DCRFresolver, and wherein the combined probability map is processed based ona unary term and a pairwise term.
 10. The computerized method accordingto claim 9, wherein the pairwise term is constructed based on anappearance kernel and a smoothness kernel.
 11. The computerized methodaccording to claim 10, wherein the appearance kernel is an edgepreserving denoiser that determines a label of a given pixel based onsimilar neighboring pixels thereof, and the smoothness kernel determinesa label of a given pixel based on neighboring pixels thereof.
 12. Thecomputerized method according to claim 1, wherein the image is ahigh-resolution review image of the specimen obtained from a reviewtool.
 13. The computerized method according to claim 1, wherein thesecond label map has enhanced repeatability as compared to a label mapgenerated from the first probability map.
 14. A computerized system ofsegmenting an image of a fabricated semiconductor specimen, the systemcomprising a processor and memory circuitry (PMC) configured to: obtaina first probability map corresponding to the image representative of atleast a portion of the fabricated semiconductor specimen and indicativeof predicted probabilities of pixels in the image to correspond to oneor more first structural elements presented in the image, wherein thefirst probability map is generated by processing the image using a deeplearning model; obtain a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; perform simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; and generatea second label map based on the first probability map and the secondprobability map, wherein the second label map is informative of one ormore segments representative of the first structural elements and labelsassociated therewith, and wherein equivalent first structural elementsare associated with the same label, the second label map being usablefor segmentation of the image with enhanced repeatability.
 15. Thecomputerized system according to claim 14, wherein the PMC is configuredto perform the simulation by: performing a distance transform on thefirst label map to obtain a distance map based on a relative distancebetween each given pixel in the first label map and a closest edgetherefrom; and transforming the distance map into the second probabilitymap informative of simulated probabilities of the pixels in the firstlabel map to correspond to the one or more segments as relative to theclosest edge.
 16. The computerized system according to claim 14, whereinthe relative distance includes, for each given pixel in the first labelmap: i) a distance between the given pixel and a closest edge therefrom,and ii) a relative position of the given pixel with respect to theclosest edge.
 17. The computerized system according to claim 16, whereinthe relative position indicates whether the given pixel is locatedwithin a given second structural element whose contour comprises theclosest edge, or outside of the given second structural element.
 18. Thecomputerized system according to claim 14, wherein the PMC is configuredto generate a second label map by: combining the first probability mapand the second probability map to obtain a combined probability map; andusing a resolver to process the combined probability map, giving rise tothe second label map.
 19. The computerized system according to claim 18,wherein the combining is performed using a predetermined weight factorindicative of a desired tradeoff level between sensitivity andrepeatability of the segmentation of the image.
 20. A non-transitorycomputer readable storage medium tangibly embodying a program ofinstructions that, when executed by a computer, cause the computer toperform a method of segmenting an image of a fabricated semiconductorspecimen, the method comprising: obtaining a first probability mapcorresponding to the image representative of at least a portion of thefabricated semiconductor specimen and indicative of predictedprobabilities of pixels in the image to correspond to one or more firststructural elements presented in the image, wherein the firstprobability map is generated by processing the image using a deeplearning model; obtaining a first label map informative of one or moresegments representative of second structural elements and labelsassociated with the segments, wherein the second structural elements arepresented in design data charactering the at least portion, and whereinequivalent second structural elements are associated with the samelabel; performing simulation on the first label map to obtain a secondprobability map indicative of simulated probabilities of pixels in thefirst label map to correspond to the one or more segments; andgenerating a second label map based on the first probability map and thesecond probability map, wherein the second label map is informative ofone or more segments representative of the first structural elements andlabels associated therewith, and wherein equivalent first structuralelements are associated with the same label, the second label map beingusable for segmentation of the image with enhanced repeatability.